An Efficient Metaheuristic Algorithm for Job Shop Scheduling in a Dynamic Environment

This paper proposes an Improved Multi-phase Particle Swarm Optimization (IMPPSO) to solve a Dynamic Job Shop Scheduling Problem (DJSSP) known as non-deterministic polynomial-time hard (NP-hard) problem. A cellular neighbor network, velocity reinitialization strategy, randomly select sub-dimension and constraint handling function are introduced in the IMPPSO. The IMPPSO is used Kundakcı Kulak problem set compared with original (MPPSO) Heuristic Kalman Algorithm (HKA). results show that has better global exploration capability convergence. improved fitness for most of benchmark instances set, average improvement rate 5.16% Genetic Algorithm-Mixed (GAM) 0.74% HKA. performance solving real-world problems verified by case study. high level operational efficiency also evaluated demonstrated proposing simulation model capable using decision-making algorithm environment.